Parallel software
Parallel software
part of the Parallel computing theme from CEGIS
Software can be built to work on multiple processors in many computers.
Two common styles of parallel computing are High-Performance Computing (HPC) focusing on making one task fast, while High Throughput Computing (HTC) handles a large number of independent tasks.
Many-task Computing (MTC) is similar to HTC but emphasizes completing many varied tasks quickly.
Evolution from Multi-Resolution Raster (MRR)
The HPC project evolved from the Multi-Resolution Raster (MRR) research project and it focuses on efficient processing of geospatial data in the supercomputing domain for the benefit of spatial analytics and spatial data handling.
This investigation is highly applicable to our research vision within CEGIS of massive, quick processing and production of National Geospatial Program (NGP) products.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
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A guide to creating an effective big data management framework
Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey...Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different...Deep learning detection and recognition of spot elevations on historic topographic maps
Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to...Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...
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CEGIS - Rolla, Missouri
CEGIS - Denver, Colorado
CEGIS - Rolla, Missouri